Intelligent Search
Vector-based semantic search that understands intent, not just keywords. Keyspider bridges the vocabulary gap between how users ask questions and how your content is written — surfacing the right result even when exact terms don't match.
How it works
Content is vectorized at index time
When Keyspider crawls your content, it generates vector embeddings for every page, document, and chunk — capturing the semantic meaning of the text.
Query is matched by meaning
When a user searches, Keyspider encodes the query into the same vector space and retrieves content by semantic proximity, not just keyword overlap.
Hybrid ranking combines signals
Semantic scores are combined with full-text BM25 ranking, recency, engagement signals, and any manual rules you've configured to produce the final result order.
Use cases
Government citizen portals
Citizens search for 'help paying my rates' and find the rates assistance programme — even though the page doesn't use the word 'help' or 'paying'.
University knowledge bases
Students searching for 'when do classes start' find term dates and enrolment deadlines without needing to know the exact page title.
Enterprise intranets
Staff find the right HR policy when searching in plain English, even across multi-brand or multi-language deployments.
Support and documentation portals
Customers describing a problem in their own words are matched to the relevant troubleshooting article, reducing support ticket volume.
Ready to give your users better answers?
AI Search, AI Assistant, and Workplace Search. Deployed in days, not months. See it live on your own content.
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